Prognostics of Combustion Instabilities from Hi-speed Flame Video using A Deep Convolutional Selective Autoencoder
Prognostics of Combustion Instabilities from Hi-speed Flame Video using A Deep Convolutional Selective Autoencoder
复制标题
使用深度卷积选择性自动编码器从高速火焰视频预测燃烧不稳定性
DOI：
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发表时间：
2020
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影响因子：
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通讯作者：
S. Sarkar
中科院分区：
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作者：
Adedotun Akintayo;Kin Gwn Lore;S. Sarkar;S. Sarkar
燃烧过程中产生的热声不稳定性会导致各种人为工程系统的严重恶化和安全问题，如陆基和空基燃气轮机。这种现象被描述为具有周期性相干涡旋脱落的不同空间尺度的自维持和大幅度压力振荡。及早发现和密切监测燃烧不稳定性是延长任何燃气轮机剩余使用寿命的关键。然而，这种迫在眉睫的稳定燃烧不稳定性由于其突发性(分叉型)性质，仅从压力数据中很难检测到。能够检测到早期不稳定发生的工具链对现代发动机的安全和性能具有变革性的影响。本文提出了一种端到端的深度卷积选择性自动编码方法，以捕捉高速火焰视频中丰富的信息，用于不稳定性预测。在这种情况下，训练自动编码器以选择性地掩蔽稳定的火焰并允许不稳定的火焰图像帧。性能比较是用著名的图像处理工具条件随机场进行的，条件随机场也被训练为具有选择性。在此背景下，导出了信息论阈值。通过一组从实验室燃烧室采集的不同工况下的真实数据对该框架进行了验证，结果表明，该框架能够有效地检测燃烧过程从稳定区域向不稳定区域的转变过程中的细微不稳定特征。∗通讯作者Adedotun Akintayo等人。这是一篇开放获取的文章，根据知识共享署名3.0美国许可证的条款分发，该许可证允许在任何媒体上不受限制地使用、分发和复制，前提是原始作者和来源必须注明。
The thermo-acoustic instabilities arising in combustion processes cause significant deterioration and safety issues in various human-engineered systems such as land and air based gas turbine engines. The phenomenon is described as selfsustaining and having large amplitude pressure oscillations with varying spatial scales of periodic coherent vortex shedding. Early detection and close monitoring of combustion instability are the keys to extending the remaining useful life (RUL) of any gas turbine engine. However, such impending instability to a stable combustion is extremely difficult to detect only from pressure data due to its sudden (bifurcationtype) nature. Toolchains that are able to detect early instability occurrence have transformative impacts on the safety and performance of modern engines. This paper proposes an endto-end deep convolutional selective autoencoder approach to capture the rich information in hi-speed flame video for instability prognostics. In this context, an autoencoder is trained to selectively mask stable flame and allow unstable flame image frames. Performance comparison is done with a wellknown image processing tool, conditional random field that is trained to be selective as well. In this context, an informationtheoretic threshold value is derived. The proposed framework is validated on a set of real data collected from a laboratory scale combustor over varied operating conditions where it is shown to effectively detect subtle instability features as a combustion process makes transition from stable to unstable region. ∗corresponding author Adedotun Akintayo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.